Merge SpendRight scraper data

Merge Scraper Data (for great justice!)

There are a lot of consumer campaigns out there on the Internet. Consumer
campaigns supported by perfectly lovely organizations, organized around
causes you wholeheartedly support, that would change the world if enough
people followed through on them.

But it's hard to put them into practice. And it's really hard to put more
than one campaign at a time into practice. Different campaigns have different
scoring systems, different names for the same company, and, when they have
them at all, different apps that don't talk to each other.

msd takes messy data from a bunch of different consumer campaigns, and
puts it into a single unified format.

Using the data

If you're not there already, check out
msd's morph.io page, where you can
view and download data merged from SpendRight's scrapers.

Keep in mind that the original consumer campaigns are generally copyrighted by
the non-profits that created them, and they have all sorts of different
terms/licensing agreements. It's up to you to decide whether to ask
them for permission now, or forgiveness later.

(This mostly applies to the claim and rating tables; facts about
companies and brands are almost certainly fair game.)

Installation

It's on PyPI: pip install msd

Usage

msd db1.sqlite [db2.sqlite ...]

This produces a file named msd.sqlite (you can change this with the -o
switch).

msd can also take YAML files as input. The YAML files should encode a
map from table name to list of rows (which are maps from column name to value).
For example:

If you don't have the library installed (e.g. for development), you
can use python -m msd.cmd in place of msd.

Data format

msd uses a SQLite data format, both for input and output.

The input and output format are almost identical; differences are noted
in italics.

Keys

Every campaign in the input data should have a campaign_id
that would work as a Python identifier (for example wwf_palm_oil).

There isn't a company_id field though; we just use the shortest name
that a company is commonly referred to by.``msd`` is smart
enough to know that, for example, The Coca-Cola Company can be called
Coca-Cola but that we can't refer to The Learning Company as simply
"Learning".

Similarly, there isn't a brand_id field, msd just figures out the
proper name for the brand (minus the ™, etc.), and puts it into the brand
field; the "key" for any given brand is company and brand together.

There also aren't (product) category keys; we just put the name of the
category (e.g. Chocolate) into the category field.

Finally, the initial data sources each get a scraper_id, which is one
or more identifiers, separated by dots (e.g. sr.campaign.wwf_palm_oil).
These serve only to help you track down problems in your input data.

Every table in the input data may have ascraper_idfield to help
identify which code gathered that data. The stem
of whatever input file data came from will be prepended to form thescraper_idin the output.

Messy input data

msdcan accept very, very messy input data. The goal is for you to be
able as little effort as possible into writing scrapers.

no primary keys

For starters, the input data need not have primary keys, or any keys at
all. The first thing we do is shovel all the input data into a single
"scratch" table anyways.

It's totally fine to have two rows that would have the same keys in the
output data;msdwill merge them for you.

missing/extra fields

It's totally fine for the input data to be missing fields, or have
fields set toNULLthat are supposed to have a value (in the worst case,
if you omit a required value,msdwill just ignore that row.

It's fine to have extra fields;msdwill just ignore them.

different names for companies and brands

It's fine to use different names for the same company
or brand;msdwill figure this out and merge them as appropriate.

general text cleanliness

For every text field,msddoes the following things for you:

converts all whitespace (tabs, double spaces, etc.) to a single space

strips leading and trailing whitespace

converts "smart quotes", ligatures, and other silliness to the plain
equivalent

normalizes all unicode into NFKD form (this basically means there aren't
multiple ways to represent the same accented character).

brand name cleaning

In addition, you can be even lazier with thebrandfield.msdautomatically finds ™, ®, etc., puts it elsewhere for safekeeping (see
thetmfield, below), and ignores anything after it.

For example, if you throw something likeINVOKANA™ (canagliflozin) USPIinto thebrandfield, it'll know
that the brand is namedINVOKANAand is supposed to have a ™ after it.

category name cleaning

msdformats category names in a consistent way. For example,food & beveragesin the input data would becomeFood and Beveragesin the output data.

rating cleanup

msd can do limited cleanup of ratings, including inferring judgment
from grade. See rating table for details.

inferred rows

msdwill infer that companies and brands exist. For example, if you
include a rating for a company in theratingtable, a corresponding
entry will be automatically created for you in thecompanytable.

and that's not all...

Nope, that's pretty much everything. Here are the table definitions:

Table definitions

brand: facts about brands

Primary Key: company, brand

brand: canonical name for the brand (e.g. Dove)

company: canonical name for the company (e.g. Unilever)

facebook_url: optional link to official Facebook page for the brand. (If
there's only a page for the company, put that in company.facebook_url).
So consumers can say nice/brutally honest things on their Facebook page.

is_former: 0 or 1. If 1, this brand no longer exists (e.g. Sanyo) or was
sold to another company (e.g. LU is no longer owned by Groupe Danone). Set
this to 1 in your input data to knock out out-of-date brand information from
out-of-date consumer campaigns.

is_licensed: 0 or 1. If 1, this brand actually belongs to another company
(e.g. The Coca-Cola Company markets products under the Evian brand).
Generally a good idea to put the responsiblity for a brand on its actual
owner.

is_prescription: 0 or 1. If 1, this brand is available by prescription
only (so you probably can't buy it on, like, Amazon.com).

logo_url: 0 or 1. Optional link to an image of this brand's logo (need not
be on the brand's website).

tm: empty string, ™, ® or ℠. The thing that companies like to
appear directly after the brand name.

twitter_handle: optional handle for the brand's Twitter account, including
the @ (e.g. @BrownCowYogurt). So consumers can congratulate them/call
them out on Twitter.

url: optional link to official web site/page for this brand. It's okay
if this is just a sub-page of the company's official website.

campaign: consumer campaigns

In practice, introducing consumer campaigns to users is one of the
most important parts of any tool you build; you'll probably want to just use
this table as a starting point, and include some content of your own.

Primary Key: campaign_id

author: optional free-form name of the organization behind the campaign
(e.g. Greenpeace International).

author_url: optional link to author's website

campaign: free-form name of the campaign (e.g.
Guide to Greener Electronics)

campaign_id: unique identifier for this campaign (e.g.
greenpeace_electronics.) Up to you to pick something that makes sense
and doesn't collide with other campaign IDs.

contributors: optional free-form description of other contributors
to the consumer campaign (e.g.
International Labor Rights Forum, Baptist World Aid).

date: optional date this campaign was created, in YYYY-MM-DD,
YYYY-MM, or YYYY format. A string, not a number. Sometimes the
best available data is a couple years old, and consumers deserve to know!

donate_url: optional link to a page where you can donate back to the
campaign/author. Try to include this somewhere in whatever you build; create a
virtuous cycle and help these consumer campaigns become financially
self-sustaining!

facebook_url: optional link to official Facebook page for the campaign,
so consumers can get involved in the movement!

goal: very brief (40 characters or less) description of what someone
helps accomplish by being involved in this campaign (e.g.
stop forced labor in Uzbekistan). Best to start this with a lowercase
letter unless the first word is a proper noun.

twitter_handle: optional handle for the campaign's Twitter account, so
that consumers can follow/reference them on Twitter. Including the @
(e.g. @WWF).

url: optional link to campaign's web site, so consumers can learn more
and get involved.

category: product categories for companies and brands

msd doesn't build an organized category tree like, say, online retailers
have; these are more like hints. See the subcategory table for details.

Primary Key: company, brand, category

brand: canonical name for the brand. Empty string if we're categorizing
a company

category: free-form name for category (e.g. Food and Beverages).

company: canonical name for the company

is_implied: 0 or 1. If 1, this category was only implied by a subcategory
relationship (see subcategory table). Ignored in the input data.

claim: bullet points to support ratings

Primary Key: campaign_id, company, brand, scope, claim

(claim is free-form, so this is more like a non-unique key)

brand: canonical name for the brand. Empty string if this is a claim
about a company.

campaign_id: unique identifier of campaign making this claim (see
campaign.campaign_id)

claim: free-form claim. Should be small enough to fit in a bullet point,
and be able to stand on its own (spell out obscure acronyms and other context).
Best to start this with a lowercase letter unless the first word is a
proper noun.

company: canonical name for the company

date: optional date this claim was made, in YYYY-MM-DD,
YYYY-MM, or YYYY format. A string, not a number.

judgment: -1, 0, or 1. Does the claim say something good (1), mixed
(0), or bad (-1) about the company or brand? Need not match the
campaign's rating. If a claim is totally neutral (e.g.
manufactures large appliances) it doesn't belong in this table at all!

scope: optional free-form limitation on which products this applies to
(e.g. Fair Trade). Usually an empty string, to mean no limitation or that
it's only not some scope elsewhere in the data (don't set this to
Non-Certified).

url: optional link to web page/PDF document etc. where this claim was made.
Some people like to see the supporting data!

company: facts about companies

Primary Key: company

company: canonical name for the company (e.g. Disney)

company_full: full, official name of the company (e.g.
The Walt Disney Company).

email: contact/feedback email for the company (e.g.
consumer.relations@adidas.com).

facebook_url: optional link to official Facebook page for the company.

feedback_url: optional link to a page where consumers can submit
feedback to the company (some companies don't like to do this by email).

hq_company: optional name of the country where this company is
headquartered (e.g. USA).

logo_url: 0 or 1. Optional link to an image of this company's logo (need
not be on the company's website).

phone: optional phone number for customer feedback/complaints (a string,
not a number)

twitter_handle: optional handle for the company's Twitter account,
including the @ (e.g. @Stonyfield).

url: optional link to official web site/page for this company.

company_name: canoncial, full, and alternate names for companies

Primary Key: company, company_name

company: canonical name for the company (e.g. Disney)

company_name: a name for the company. can be the canonical
name, the full name (see company.company_full) or something else
(e.g. Walt Disney).

is_alias: 0 or 1. If 1, this is a name that somebody used somewhere
but isn't really a recognizable name for the company (e.g. "AEO" for
American Eagle Outfitters or "LGE" for "LG Electronics"). Set this your
input data to knock out weird company aliases.

is_full: 0 or 1. If 1, this is the full name for the company,
which also appears in company.company_full. (There isn't an
is_canonical field; just check if company = company_name.)

rating: campaigns' judgments of brands and companies

This is where the magic happens.

brand: canonical name for the brand. Empty string if this is a rating of
a company.

judgment: -1, 0, or 1. Should consumers support (1), consider
(0), or avoid (-1) the company or brand? Some campaigns will give
everything a 1 (e.g. certifiers) or everything a -1 (e.g. boycott
campaigns).

msdcan inferjudgmentfromgrade, but otherwise you need
to set it yourself in the input data.

Red for avoid, yellow for consider, and green for support is a de-facto
standard among consumer campaigns. If all else fails, contact the campaign's
author and ask.

max_score: if score is set, the highest score possible on the rating
scale (a number).

rank: if campaign ranks companies/brands, where this one ranks
(this is an integer, and the best ranking is 1, not 0).

scope: optional free-form limitation on which products this applies to
(e.g. Fair Trade). Usually an empty string, to mean no limitation or that
it's only not some scope elsewhere in the data (don't set this to
Non-Certified).

score: optional numerical score (e.g. 57.5).

url: optional link to web page/PDF document etc. where this rating was
made. Some people like to see the supporting data!

scraper: when data was last gathered

Primary Key: scraper_id

last_scraped: when this data was last gathered, as a UTC ISO timestamp
(for example, 2015-08-03T20:55:36.795227Z).

scraper_id: unique identifier for the scraper that gathered this data

scraper_brand_map: names of brands in the input data

This is mostly useful for debugging your output data.

msdignores this table if it appears in the input data

Primary Key: scraper_id, scraper_company, scraper_brand

Other Indexes: (company, brand)

brand: canonical name for the brand. (This should never be empty;
that's what scraper_company_map is for.)

company: canonical name for the company

scraper_brand: name used for the brand in the input data

scraper_company: name used for the company in the input data

scraper_id: unique identifier for the scraper that used this
brand and company name

scraper_category_map: names of categories in the intput data

This is mostly useful for debugging your output data.

msdignores this table if it appears in the input data

Primary Key: scraper_id, category, scraper_brand

Other Indexes: (category)

category: canonical name for a category (e.g. Food and Beverages)

scraper_brand: name used for the brand in the input data (e.g.
`` food & beverages``).

scraper_id: unique identifier for the scraper that used this
category name

scraper_company_map: names of companies in the input data

This is mostly useful for debugging your output data.

msdignores this table if it appears in the input data

Primary Key: scraper_id, scraper_company

Other Indexes: (company)

company: canonical name for the company

scraper_brand: name used for the brand in the input data

scraper_id: unique identifier for the scraper that used this
company name

subcategory: product category relationships

msd doesn't attempt to build a proper category tree; it's really just
a directed graph of category relationships: if something is in category
A (subcategory) it must also be in category B (category).

msdautomatically infers implied relationships: if A is a subcategory
of B and B is a subcategory of C, A is a subcategory of C.

category: canonical name for a category

is_implied: 0 or 1. If 1, this relationship was inferred by msd.
Ignored in the input data.

subcategory: canonical name for a subcategory of category

url: hook for scraping URLs in the scraper data

This table only exists in the input data, and is only used to fill fields
in the output data that would otherwise be empty.

This allows us to build generic scrapers that can grab Twitter handles,
Facebook URLs, etc. directly from a company or brand's official page. See
SpendRight's scrape-urls
for an example.

facebook_url: optional facebook page for a company/brand

last_scraped: when the company/brand's page was scraped, as a UTC
iso timestamp (e.g. 2015-08-03T20:55:36.795227Z). Not currently used.

twitter_handle: optional twitter handle for a company/brand, including
the leading @.

url: url this data was scraped from

Writing your own scrapers

If you want to write something in Python, check out SpendRight's
scrape-campaigns
project, and submit a pull request (look in scrapers/) for examples.

If you'd rather write in another language, consider setting up your own
scraper on morph.io, which can also handle scrapers
in Ruby, PHP, Perl, and Node.js. See the
morph.io Documentation for details.
And let us know, so we can point
msd's morph.io page at it.

Working on msd

msd is pretty straightforward. Here's a brief overview of how it works:

msd starts in msd/cmd.py (look for msd.cmd.run()).

It first dumps all the input data into a temporary "scratch" DB
(msd-scratch.sqlite) with the correct columns and useful indexes (look
for msd.scratch.build_scratch_db()).

Then it creates the output database (msd.sqlite) and fills it table by
table (look for msd.fill_output_db()).

Also, table definitions live in msd/table.py.

Using msd as a library

msd isn't really a library, but there's some useful stuff in msd
(for example, msd/company.py knows how to strip all the various versions
of "Inc." off company names).

If you want to call some of this stuff from another project, please let us
know so that we can work out a sane, stable interface for you!

Although ABF purchases its cocoa for European cocoa-based drinks from UTZ and International Cocoa Alliance sources, ABF does not elucidate how much of a market share this represents overall in its business operations.

ABF indicates that its cocoa for its European cocoa-based drinks (including Ovomaltine) is sourced from UTZ certified cocoa, which encourages its farmers to partake in environmentally-conscious and sustainable farming practices.

ABF remains a committed member of the Roundtable on Sustainable Palm Oil (RSPO). As a member, ABF will continue to ensure an increased supply of Certified Sustainable palm oil and their requisite facilities, by 2015. Given necessary supplies, all businesses will use only Certified Sustainable Palm Oil.

Associated British Foods (ABF) subsidiary company Wander AG which produces Ovomaltine has taken several policy measures to reduce carbon emissions, such as the use of renewable energy and energy efficiency measures.

Wander AG (producer of Ovomaltine) reports to further reduce its GHG emissions by 15% until 2020. Also, Wander AG reports to be "free of CO2". However, neither details on that nor a base year for its reduction goal by 2020 are provided.

ABF communicates that it is involved with the International Cocoa Initiative and that all of its cocoa based drinks sold in Europe are UTZ certified Sustainable Cocoa certified. However, there is no specific mention of signing an agreement to ensure 100% of its products are certified by 2020.